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Fukuoka Institute of Technology Repository >
02.University Bulletins & Report >
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Vol.1 >
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| Title | : | Echo state network による時系列からの非定常性の検出 |
| Authors | : | 山口 裕
兒嶋 大也
宮嵜 鋼 |
| Issue Date | : | Dec-2018 |
| Abstract | : | We developed a novel method that detecting nonstationarity in observed time-series. Using reservoir computing
approach, which now becomes a popular tool in machine learning field, we estimated cross-prediction errors
between different segments of time-series. These errors are considered as a measure of non-similarities between
different segments. Then, multidimensional scaling was used to obtain a reduced representation of relational
patterns among segments. |
| Type Local | : | 紀要論文 |
| ISSN | : | 24345725 |
| Publisher | : | 福岡工業大学総合研究機構 |
| Comment | : | 情報科学研究所(Computer Science Laboratory) |
| URI | : | http://hdl.handle.net/11478/1219 |
| citation | : | 福岡工業大学総合研究機構研究所所報
1
43
46 |
| Citation | : | 福岡工業大学総合研究機構研究所所報 Vol.1 p.43 -46 |
| Appears in Collections | : | Vol.1
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Files in This Item:
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Size | Format |
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| 11478-1219_p43.pdf | | 790Kb | Adobe PDF | View/Open |
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